Skip to main content
SpringerLink
Log in

Analysis and experimental evaluation of image-based PUFs

  • Regular Paper
  • Published:
Journal of Cryptographic Engineering Aims and scope Submit manuscript

Abstract

Physically unclonable functions (PUFs) arebecoming popular tools for various applications, such as anti-counterfeiting schemes. The security of a PUF-based system relies on the properties of its underlying PUF. Usually, evaluating PUF properties is not simple as it involves assessing a physical phenomenon. A recent work (Armknecht et al. in A formalization of the security features of physical functions. In: IEEE Symposium on Security and Privacy, pp. 397–412, 2011) proposed a generic security framework of physical functions allowing a sound analysis of security properties of PUFs. In this paper, we specialize this generic framework to model a system based on a particular category of PUFs called image-based PUFs. These PUFs are based on random visual features of the physical objects. The model enables a systematic design of the system ingredients and allows for concrete evaluation of its security properties, namely and physical unclonability which are required by anti-counterfeiting systems. As a practical , the components of the model are instantiated by Laser-Written PUF, White Light Interferometry evaluation, two binary image hashing procedures namely, Random Binary Hashing and Gabor Binary Hashing, respectively, and code-offset fuzzy extraction. We experimentally evaluate security properties of this example for both image hashing methods. Our results show that, for this particular example, adaptive image hashing outperforms the non-adaptive one. The experiments also confirm the usefulness of the formalizations provided by Armknecht et al. (A formalization of the security features of physical functions. In: IEEE Symposium on Security and Privacy, pp. 397–412, 2011) to a practical example. In particular, the formalizations provide an asset for evaluating the concrete trade-off between robustness and physical unclonability. To the best of our knowledge, this experimental evaluation of explicit trade-off between robustness and physical unclonability has been performed for the first time in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. A mathematical procedure that yields the same challenge-response behavior as the PUF e.g., a fake image.

  2. For detailed description of each component refer to [1].

  3. Note that most PUFs are mathematically clonable when using a fixed challenge.

  4. Off course, the validity of this assumption should still be asserted by the system designer when selecting a specific PUF realization.

  5. Images are represented as vectors, e.g., by concatenating their rows.

  6. This contributes together with the fuzzy extraction to provide the same value for the same PUF and independent values for different PUFs.

  7. The overlap between distributions can be inspected visually or by means of more precise measures e.g., Kullback-Leibler divergence between two distributions.

  8. Roughly speaking, incoherence means that no element of one basis has a sparse representation in terms of the other basis.

  9. Although, according to [37], more precise name for this scheme would be code-offset secure sketch, we prefer to be consistent with more commonly used term in literature.

  10. i.i.d stands for Independent and Identically Distributed random variable.

  11. In absolute value sense.

  12. Whereby the counts are replaced by the normalized counts such that the maximum frequency equals 1.

  13. As parsing all different combinations of parameters is combinatorially complex, we adjust the parameters by experimentally tuning them to get our results.

  14. The reason to select \(M=255\) will be justified later in in this section.

References

  1. Armknecht, F., Maes, R., Sadeghi, A.R., Standaert, F.X., Wachsmann, C.: A formalization of the security features of physical functions. In: IEEE Symposium on Security and Privacy, pp. 397–412 (2011)

  2. Bastia, S.: Next generation technologies to combat counterfeiting of electronic components. IEEE Trans. Compon. Packag. Tech. 25, 175–176 (2002)

    Article  Google Scholar 

  3. Chong, C.N., et al.: Anti-counterfeiting with a random pattern. In: International Conference on Emerging Security Information, Systems and Technology, pp. 146–153 (2008)

  4. Bauder, D.W.: An anti-counterfeiting concept for currency systems. Technical Report PTK-11990, Sandia National Labs, Albuquerque, NM (1983)

  5. Commission on Engineering Committee on Next-Generation Currency Design and National Research Council Technical Systems. Counterfeit Deterrent Features for the Next-Generation Currency Design. The National Academies Press, Washington (1993)

  6. Pappu, R.: Physical One-Way Functions. PhD thesis, MIT Press, Cambridge (2001)

  7. Pappu, R.S., Recht, B., Taylor, J., Gershenfeld, N.: Physical one-way functions. Science 297, 2026–2030 (2002)

    Article  Google Scholar 

  8. Gassend, B., Clarke, D., van Dijk, M., Devadas, S.: Silicon physical random functions. In: ACM conference on Computer and Communications Security (2002)

  9. Tuyls, P., Schrijen, G.J., Skoric, B., van Geloven, J., Verhaegh, N., Wolters, R: Read-proof hardware from protective coatings. In: Cryprographic Hardware and Embedded Systems Workshop. LNCS, vol. 4249, pp. 369–383. Springer, Berlin (2006)

  10. Lim, D., Lee, J.W., Gassend, B., Suh, G.E., van Dijk, M., Devadas, S.: Extracting secret keys from integrated circuits. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 13(10), 1200–1205 (2005)

    Article  Google Scholar 

  11. Lee, J.W., Lim, D., Gassend, B., Suh, G.E., van Dijk, M., Devadas, S.: A technique to build a secret key in integrated circuits for identification and authentication applications. In: VLSI Circuits. Digest of Technical Papers, pp. 176–179 (2004)

  12. Guajardo, J., Kumar, S.S., Schrijen, G.J., Tuyls, P.: FPGA intrinsic PUFs and their use for IP protection. In: Workshop on Cryptographic Hardware and Embedded Systems (CHES). LNCS, vol. 4727, pp. 63–80 (2007)

  13. Metois, E., Yarin, P., Salzman, N., Smit, J.R.: Fiberfingerprint identification. In: Workshop on Automatic Identification, pp. 147–154 (2002)

  14. Buchanan, J.D.R., Cowburn, R.P., Jausovec, A.V., Petit, D., Seem, P., Xiong, G., Atkinson, D., Fenton, K., Allwood, D.A., Bryan, M.T.: Fingerprinting documents and packaging. Nature 475 (2005)

  15. Clarkson, W., Weyrich, T., Finkelstein, A., Heninger, N., Halderman, J.A., Felten, E.W.: Fingerprinting blank paper using commodity scanners. In: Proceedings of the 2009 30th IEEE Symposium on Security and Privacy, pp. 301–314 (2009)

  16. Sharma, A., Subramanian, L., Brewer, E.A.: Paperspeckle: microscopic fingerprinting of paper. In: Proceedings of the 18th ACM conference on Computer and communications security, CCS ’11, pp. 99–110. ACM, New York (2011)

  17. Shariati, S., Standaert, F.-X., Jacques, L., Macq, B., Salhi, M.A., Antoine, P.: Random profiles of laser marks. In: WIC Symposium on Information Theory in the Benelux, pp. 27–34 (2010)

  18. Tuyls, P., Skoric, B., Kevenaar, T.: Security with Noisy Data. Springer, Berlin (2007)

    Book  MATH  Google Scholar 

  19. Maes, R., Verbauwhede, I.: Physically unclonable functions: a study on the state of the art and future research directions. In: Towards Hardware-Intrinsic Security, Information Security and Cryptography, pp. 3–37. Springer, Berlin (2010)

  20. Tuyls, P., Škorić, B.: Strong authentication with physical unclonable functions. In: Security, Privacy, and Trust in Modern Data Management, pp. 133–148 (2007)

  21. Tuyls, P., et al.: Secure Key Storage and Anti-Counterfeiting, pp. 255–268 Springer, Berlin (2008)

  22. Tuyls, P., Batina, L.: RFID-tags for anti-counterfeiting. In: Topics in Cryptology—CT-RSA 2006. LNCS, vol. 3860, pp. 115–131. Springer, Berlin (2006)

  23. Bulens, P., Standaert, F.-X., Quisquater, J.-J.: How to strongly link data and its medium: the paper case. IET Inf. Secur. 4(2), 125–136 (2010)

    Article  Google Scholar 

  24. Kirovski, D. Anti-counterfeiting: mixing the physical and the digital world. In: Foundations for Forgery-Resilient Cryptographic Hardware. Dagstuhl Seminar Proceedings, vol. 09282 (2010)

  25. Shariati, S., Koeune, F., Standaert, F.-X.: Security analysis of image-based PUFs for anti-counterfeiting. In: Communications and Multimedia Security, pp. 27–34. Springer, Berlin (2012)

  26. Skoric, B., Tuyls, P., Ophey, W.: Robust key extraction from physical uncloneable functions. In: Applied Cryptography and Network Security (ACNS), pp. 407–422 (2005)

  27. Lim, D., Lee, J.W., Gassend, B., Edward Suh, G., van Dijk, M., Devadas, S.: Extracting secret keys from integrated circuits. IEEE Trans. VLSI Syst. 13(10), 1200–1205 (2005)

    Article  Google Scholar 

  28. Armknecht, F., Maes, R., Sadeghi, A.-R., Sunar, B., Tuyls, P.: Memory leakage-resilient encryption based on physically unclonable functions.In Advances in Cryptology (ASIACRYPT). LNCS, vol. 5912, pp. 685–702 (2009)

  29. Baoshi, Z., Jiankang, W., Kankanhalli, M.S.: Print signatures for document authentication. In: ACM Conference on Computer and Communications Security, pp. 145–153 (2003)

  30. Kirovski, D.: Toward an automated verification of certificates of authenticity. In: Proceedings of the 5th ACM conference on Electronic commerce, EC ’04, pp. 160–169. ACM, New York (2004)

  31. Chen, Y., Mihçak, K., Kirovski, D.: Certifying authenticity via fiber-infused paper. SIGecom Exch. 5, 29–37 (April 2005)

  32. Chong, C.N., Jiang, D.: Anti-counterfeiting using phosphor puf. In: International Conference on In Anti-Counterfeiting, pp. 59–62 (2008)

  33. Beekhof, F., Voloshynovskiy, S., Koval, O., Villán, R.: Secure surface identification codes. In: Steganography, and Watermarking of Multimedia Contents X. Proceedings of SPIE, vol. 6819 (2008)

  34. Tuyls, P., Skoric, B.: Secret key generation from classical physics. In: Philips Research Book Series (2005)

  35. Shariati, S., Jacques, L., Standaert, F.-X., Macq, B., Salhi, M.A., Antoine, P.: Randomly driven fuzzy key extraction of uncloneable images. In: International Conference on Image Processing (ICIP) (2010)

  36. Juels, A., Wattenberg, M.: A fuzzy commitment scheme. In: ACM Conference on Computer and Communications Security (1999)

  37. Dodis, Y., et al.: Fuzzy extractors: How to generate strong secret keys from biometrics and other noisy data. In: Eurocrypt’04, pp. 523–540 (2004)

  38. Mallat, S.: A wavelet tour of signal processing: the sparse way, 3rd edn. Academic Press, New York (2008)

  39. Jacques, L., Duval, L., Chaux, C., Peyré, G.: A panorama on multiscale geometric representations, intertwining spatial, directional and frequency selectivity. Signal Process. 91, 2699–2730 (2011)

    Google Scholar 

  40. Laska, J.N., Kirolos, S., Duarte, M.F., Ragheb, T., Baraniuk, R.G., Massoud, Y.: Theory and implementation of an analog-to-information converter using random demodulation. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1959–1962 (2007)

  41. Candes, E.J., Romberg, J.: Quantitative robust uncertainty principles and optimally sparse decompositions. Found. Comput. Math. 6, 227–254 (April 2006)

  42. Tsaig, Y., Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)

    Article  Google Scholar 

  43. Ignatenko, T.: Secret-Key Rates and Privacy Leakage in Biometric Systems. PhD thesis, TU Eindhoven (2009)

  44. Huffman, W.C., Pless, V.: Fundamentals of Error-Correcting Codes. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

  45. Baraniuk, R., Davenport, M., DeVore, R., Wakin, M.: A simple proof of the restricted isometry property for random matrices. Constr. Approx. 28, 253–263 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  46. Goemans, M., Williamson, D.: Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. ACM 42, 1145 (1995)

    MathSciNet  Google Scholar 

  47. Jacques, L., Laska, J. N., Boufounos, P.T., Baraniuk, R.G.: Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors. ArXiv e-prints (2011)

  48. Duarte, M.F., Davenport, M.A., Takhar, D., Laska, J.N., Sun, T., Kelly, K.F., Baraniuk, R.G.: Single-pixel imaging via compressive sampling. IEEE Signal Proc. Mag 25, 83–91 (2008)

    Article  Google Scholar 

  49. Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)

    Article  Google Scholar 

  50. Malacara, D.: Optical Shop Testing, 2nd edn. Wiley, New York (1992)

  51. Wyant, J.C.: White light interferometry. In: Conference on Holography (SPIE) (2002)

  52. Vincent, L.: Grayscale area openings and closings, their efficient implementation and applications. pp. 22–27 (1993)

  53. Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2, 176–201 (1993)

    Article  Google Scholar 

  54. Naini, F.M., Gribonval, R., Jacques, L., Vandergheynst, P.: Compressive sampling of pulse trains: spread the spectrum! In: IEEE International Conference on Acoustics Speech Signal Processing, pp. 2877–2880 (2009)

  55. Puy, G., Vandergheynst, P., Gribonval, R., Wiaux, Y.: Universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques. CoRR, abs/1110.5870 (2011)

  56. David, W.: SCOTT. On optimal and data-based histograms. Biometrika 66(3), 605–610 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  57. Cappelli, Raffaele, Maio, Dario, Maltoni, Davide, Wayman, James L., Jain, Anil K.: Performance evaluation of fingerprint verification systems. IEEE Trans. Pattern Anal. Mach. Intell. 28, 3–18 (2006)

    Article  Google Scholar 

Download references

Acknowledgments

We thank François Koeune and Roel Maes for the helpful remarks. This research work was supported by the Belgian Walloon Region project TRACEA. François-Xavier Standaert and Laurent Jacques are Associate Researchers of the Belgian Fund for Scientific Research (FNRS-F.R.S.). This work has been funded in part by the ERC project 280141 (acronym CRASH).

Author information

Authors and Affiliations

  1. ICTEAM Institute, Université Catholique de Louvain, Place du Levant 3, 1348 , Louvain-la-Neuve, Belgium

    Saloomeh Shariati, François-Xavier Standaert, Laurent Jacques & Benoit Macq

Authors
  1. Saloomeh Shariati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. François-Xavier Standaert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laurent Jacques
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Benoit Macq
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saloomeh Shariati.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shariati, S., Standaert, FX., Jacques, L. et al. Analysis and experimental evaluation of image-based PUFs. J Cryptogr Eng 2, 189–206 (2012). https://doi.org/10.1007/s13389-012-0041-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13389-012-0041-3

Keywords

Search

Navigation